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《Journal of Optoelectronics.Laser》 2012-02
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SVM parameter optimization and application based on artificial bee colony algorithm

YU Ming,AI Yue-qiao(School of Computer Science and Software,Hebei University of Technology,Tianjin 300401,China)  
In order to solve the difficult problem of falling into local optimal solution which all the common support vector machine(SVM) parameter optimization methods have in different degree,a new SVM parameter optimization method based on artificial bee colony(ABC) algorithm is proposed and applied to computer handwriting verification.Penalty factor C and kernel function parameter σ of SVM were taken as the optimization objects,and the classification accuracy of SVM was used as the fittness value.Tests on four standard datasets show that compared with colony algorithm,practical swarm algorithm and genetic algorithm,the proposed method overcomes the local optimal solution problem,achieves higher classification accuracy,and decreased the running time efficiently in small number classification problems.Then the proposed method was applied to handwriting verification.Compared with the SVM optimized by the other three optimization algorithms,the method reposed in this paper obtains higher classification accuracy.
【Fund】: 国家科技支撑计划资助项目(2009BAI71B02);; 河北省科技支撑计划资助项目(10213565)
【CateGory Index】: TP181
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